Senior Software Engineer, SageMaker AI Studio

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, BELLEVUE - 168,100.00 - 227,400.00 USD annually

# Job Summary – Senior Software Development Engineer, SageMaker AI Studio

Amazon Web Services (AWS) is seeking a Senior Software Development Engineer to join the SageMaker AI Studio team. SageMaker AI Studio is the industry-leading integrated development environment (IDE) for machine learning and generative AI, empowering millions of developers and data scientists to build, train, and deploy models at scale.

As a Senior SDE on this team, you will own the design and delivery of large-scale, highly available distributed systems that power the end-to-end ML development experience. You will drive technical vision, influence product direction, and raise the engineering bar across the organization. You will work at the intersection of cloud infrastructure, developer tooling, and AI/ML—solving complex problems that directly impact how customers innovate with machine learning.

## Key Responsibilities

- **Technical Leadership** – Lead the architecture, design, and implementation of complex software systems. Define long-term technical strategy and drive alignment across teams.
- **System Design & Delivery** – Own end-to-end delivery of large-scale distributed services with high availability, low latency, and operational excellence.
- **Cross-Team Influence** – Collaborate with product, science, and partner engineering teams to define roadmaps and deliver customer-obsessed solutions.
- **Mentorship & Bar Raising** – Mentor engineers across levels, conduct design and code reviews, and actively raise the technical and operational bar for the team.
- **Operational Excellence** – Champion best practices in CI/CD, observability, testing, and incident management. Drive a culture of ownership and accountability.
- **Innovation** – Identify opportunities to simplify, automate, and improve the developer experience. Contribute to patents, publications, or open-source projects where appropriate.
- **Customer Obsession** – Deeply understand customer workflows and pain points; translate insights into scalable, intuitive platform capabilities.

## Basic Qualifications

- 7+ years of professional software development experience
- 5+ years of professional experience designing and building large-scale distributed systems
- Proficiency in one or more modern programming languages (e.g., Java, Python, TypeScript, Go)
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience)

## Preferred Qualifications

- Experience building developer tools, IDEs, or ML/AI platforms
- Deep understanding of cloud-native architectures (containers, server-less, micro-services)
- Track record of delivering ambiguous, high-impact projects with minimal guidance
- Experience mentoring senior engineers and influencing technical direction across an organization
- Familiarity with ML workflows (training, inference, model management) and the challenges practitioners face
- Master's or PhD in Computer Science or related field